Pub Date : 2011-06-01DOI: 10.30016/JGS.201106.0002
Yuran Liu, Mao-kang Luo, Hong Ma, Mingliang Hou
In order to improve the correlation accuracy of DengShi correlation algorithm, the fractional order correlation algorithm of multiple uncertain time sequences is proposed in this paper. By taking advantage of the memory property of fractional order, the algorithm introduces the measurement of fractional order differential for the local trend of time sequence into the correlation algorithm and also analyzes the influences of differential order and noise upon correlation accuracy, provides selection relations between noise level and order. It has been proven with examples that the correlation accuracy of fractional order correlation algorithm has increased by two orders of magnitude as compared with DengShi correlation algorithm.
{"title":"Fractional Order Correlation Algorithm of Uncertain Time Sequence","authors":"Yuran Liu, Mao-kang Luo, Hong Ma, Mingliang Hou","doi":"10.30016/JGS.201106.0002","DOIUrl":"https://doi.org/10.30016/JGS.201106.0002","url":null,"abstract":"In order to improve the correlation accuracy of DengShi correlation algorithm, the fractional order correlation algorithm of multiple uncertain time sequences is proposed in this paper. By taking advantage of the memory property of fractional order, the algorithm introduces the measurement of fractional order differential for the local trend of time sequence into the correlation algorithm and also analyzes the influences of differential order and noise upon correlation accuracy, provides selection relations between noise level and order. It has been proven with examples that the correlation accuracy of fractional order correlation algorithm has increased by two orders of magnitude as compared with DengShi correlation algorithm.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-01DOI: 10.30016/JGS.201106.0005
Yu-lung Hsieh, K. Linsenmair
In this paper the Verhulst Grey Model is applied to predict spider diversity dynamics in the Wurzburg University Forest, Germany. Here, we use a moving forecasting to predict the following biodiversity values: Margalef Species Richness, Fisher Alpha Index, Simpson Index and Evenness. Among these, the Fisher Alpha Index revealed a decreasing trend in the temporal dynamic across years. Our application of the model for prediction can help lower the cost of studying biodiversity patterns and provide a crucial baseline reference for improving forest management policy.
{"title":"Biodiversity Prediction by Applying Verhulst Grey Model (GM 1,1)","authors":"Yu-lung Hsieh, K. Linsenmair","doi":"10.30016/JGS.201106.0005","DOIUrl":"https://doi.org/10.30016/JGS.201106.0005","url":null,"abstract":"In this paper the Verhulst Grey Model is applied to predict spider diversity dynamics in the Wurzburg University Forest, Germany. Here, we use a moving forecasting to predict the following biodiversity values: Margalef Species Richness, Fisher Alpha Index, Simpson Index and Evenness. Among these, the Fisher Alpha Index revealed a decreasing trend in the temporal dynamic across years. Our application of the model for prediction can help lower the cost of studying biodiversity patterns and provide a crucial baseline reference for improving forest management policy.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-01DOI: 10.30016/JGS.201106.0003
Xiang-Ling Li, Yong Wei
Under the axiomatic system of buffer operator in grey system theory, the paper constructed some new strengthening buffer operators on the basis of inverse function .Meanwhile, the reason of some strengthening buffer operators may decrease the predicted precision is studied. A new method combined strengthening buffer operators with the optimized model that adapts to high-growth data sequence is suggested. A practical example shows the validity and feasibility of the method.
{"title":"A Kind of New Strengthening Buffer Operator and the Selection of Grey Model","authors":"Xiang-Ling Li, Yong Wei","doi":"10.30016/JGS.201106.0003","DOIUrl":"https://doi.org/10.30016/JGS.201106.0003","url":null,"abstract":"Under the axiomatic system of buffer operator in grey system theory, the paper constructed some new strengthening buffer operators on the basis of inverse function .Meanwhile, the reason of some strengthening buffer operators may decrease the predicted precision is studied. A new method combined strengthening buffer operators with the optimized model that adapts to high-growth data sequence is suggested. A practical example shows the validity and feasibility of the method.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70060222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-03-01DOI: 10.30016/JGS.201103.0002
Xin Gao, Haifei Ma
With high concentration of energy consumption industry, high oil dependence and lack of corresponding bargaining and pricing strategy, China has a high probability to be hijacked by oil price with huge fluctuations, thus oil price early-warning and risk management system is needed to reduce potential loss caused by oil price fluctuations. The primary task of early-warning is forecasting, but previous projections are all based on annual or monthly data and there is lag in forecasting and early warning results. So, in order to perceive price risk within a short time and take immediate measures, this article temporarily puts aside long-term oil price factors and analyzes oil prices in a new short-term perspective and distinct proportions, then constructs a model between oil price and factors, and forecasts volatility range of oil price through combination of Co-integration and Grey theory, and proposes oil price risk management measures in high price areas for the state and oil companies.
{"title":"International Petroleum Price Risk Early-warning Based on Grey Theory","authors":"Xin Gao, Haifei Ma","doi":"10.30016/JGS.201103.0002","DOIUrl":"https://doi.org/10.30016/JGS.201103.0002","url":null,"abstract":"With high concentration of energy consumption industry, high oil dependence and lack of corresponding bargaining and pricing strategy, China has a high probability to be hijacked by oil price with huge fluctuations, thus oil price early-warning and risk management system is needed to reduce potential loss caused by oil price fluctuations. The primary task of early-warning is forecasting, but previous projections are all based on annual or monthly data and there is lag in forecasting and early warning results. So, in order to perceive price risk within a short time and take immediate measures, this article temporarily puts aside long-term oil price factors and analyzes oil prices in a new short-term perspective and distinct proportions, then constructs a model between oil price and factors, and forecasts volatility range of oil price through combination of Co-integration and Grey theory, and proposes oil price risk management measures in high price areas for the state and oil companies.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-03-01DOI: 10.30016/JGS.201103.0005
H. Yong, Yong Wei
Based on the principle of GM (1, 1) model, firstly, this article advances the basic form of non-equigap DGM (2, 1) model. Secondly, on the assumption of getting non-equigap series' 1-AGO series by accumulating, let the prediction series obey the form of nonhomogeneous exponent, this article optimizes the grey derivative and background value of non-equigap DGM (2, 1) model by calculating the definite integral of the whitened differential equation, and then, establishes a new non-equigap DGM (2, 1) model. The new model breaks through the limitations of the non-equigap series' prediction, which only obeys homogeneous exponential law, and it improves the fitting precision and prediction precision. Furthermore, it has enlarged the application of GM (1, 1).
{"title":"The Optimization of the Non-equigap DGM (2, 1) Model","authors":"H. Yong, Yong Wei","doi":"10.30016/JGS.201103.0005","DOIUrl":"https://doi.org/10.30016/JGS.201103.0005","url":null,"abstract":"Based on the principle of GM (1, 1) model, firstly, this article advances the basic form of non-equigap DGM (2, 1) model. Secondly, on the assumption of getting non-equigap series' 1-AGO series by accumulating, let the prediction series obey the form of nonhomogeneous exponent, this article optimizes the grey derivative and background value of non-equigap DGM (2, 1) model by calculating the definite integral of the whitened differential equation, and then, establishes a new non-equigap DGM (2, 1) model. The new model breaks through the limitations of the non-equigap series' prediction, which only obeys homogeneous exponential law, and it improves the fitting precision and prediction precision. Furthermore, it has enlarged the application of GM (1, 1).","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-03-01DOI: 10.30016/JGS.201103.0001
J. Min, H. Tang
On July 18, 2008, Chinese tourists obtained official permits from the R.O.C. government to visit Taiwan. This policy was of historic significance, as it indicated that cross-strait relations had turned a new leaf after several turbulent decades. Due to limited data set, and changes on the economic, financial and political environment, information thus tends to be either sufficient or indefinite under such circumstances which grey theory can flexibly deal with the fuzziness situation in the current study. The main objective of this study is therefore to obtain more accurate forecasts of Chinese tourists by the GM(1,1) interval prediction model. This study lays the groundwork for future research in model building for the purpose of estimation, and the results offer useful insights for authorities, practitioners, and policymakers in the tourism industry.
{"title":"Forecasting Chinese Tourism Demand in Taiwan Using GM(1,1) Interval Prediction Model","authors":"J. Min, H. Tang","doi":"10.30016/JGS.201103.0001","DOIUrl":"https://doi.org/10.30016/JGS.201103.0001","url":null,"abstract":"On July 18, 2008, Chinese tourists obtained official permits from the R.O.C. government to visit Taiwan. This policy was of historic significance, as it indicated that cross-strait relations had turned a new leaf after several turbulent decades. Due to limited data set, and changes on the economic, financial and political environment, information thus tends to be either sufficient or indefinite under such circumstances which grey theory can flexibly deal with the fuzziness situation in the current study. The main objective of this study is therefore to obtain more accurate forecasts of Chinese tourists by the GM(1,1) interval prediction model. This study lays the groundwork for future research in model building for the purpose of estimation, and the results offer useful insights for authorities, practitioners, and policymakers in the tourism industry.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.30016/JGS.201012.0005
Ming-Yuan Hsieh, C. Kung, Chih-Sung Lai, Wen-Ming Wu
In the modern economic era of lower profits, financial negative influence has been in the supply chain management for quite some time however, only a few assessable measurements of financial negative influence are considered. The integrated methodology of the Analytical Network Process (ANP) and the Grey Relation Analysis (GRA) is selected to evaluate key financial assessment criteria through brainstorming, focus group, the Delphi method and nominal group technique to improve the selection of suppliers in supply chain management (SCM). The specific feature of the ANP and GRA- ANP models are both to establish pairwise compared matrix and furthermore, to calculate the priority vector weights (eigenvector) of each assessable characteristic, criteria and attribute. Additionally, in the content, the analytical hierarchical relations are definitely expressed in four levels including between each characteristic of supply chain, criterion and attribute. Moreover, based on the empirical analysis, the enterprises are able to choose the best potential suppliers through this research in order to minimize financial negative influence from a financial perspective through the comparison between the ANP and GRA-ANP approaches. Finally, some suggestions for managers and researchers are inductively formed to further the best development of operation strategy of supply chain management in order to diminish financial negative influence.
{"title":"Decreasing Financial Negative Influence in the Supply Chain Management through Integrated Comparison the ANP and GRA-ANP Models","authors":"Ming-Yuan Hsieh, C. Kung, Chih-Sung Lai, Wen-Ming Wu","doi":"10.30016/JGS.201012.0005","DOIUrl":"https://doi.org/10.30016/JGS.201012.0005","url":null,"abstract":"In the modern economic era of lower profits, financial negative influence has been in the supply chain management for quite some time however, only a few assessable measurements of financial negative influence are considered. The integrated methodology of the Analytical Network Process (ANP) and the Grey Relation Analysis (GRA) is selected to evaluate key financial assessment criteria through brainstorming, focus group, the Delphi method and nominal group technique to improve the selection of suppliers in supply chain management (SCM). The specific feature of the ANP and GRA- ANP models are both to establish pairwise compared matrix and furthermore, to calculate the priority vector weights (eigenvector) of each assessable characteristic, criteria and attribute. Additionally, in the content, the analytical hierarchical relations are definitely expressed in four levels including between each characteristic of supply chain, criterion and attribute. Moreover, based on the empirical analysis, the enterprises are able to choose the best potential suppliers through this research in order to minimize financial negative influence from a financial perspective through the comparison between the ANP and GRA-ANP approaches. Finally, some suggestions for managers and researchers are inductively formed to further the best development of operation strategy of supply chain management in order to diminish financial negative influence.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-09-01DOI: 10.30016/JGS.201009.0002
Jian-Tao Chen, Yunhua Li
Longitudinal ventilation system of the long tunnel in the highway is a random, sluggish and nonlinear system. To accurately predict air pollution concentration in the road tunnel is very useful and necessary for us to improve the efficiency and the quality of ventilation control system. In this paper, based on having thoroughly analyzed the physical process of the longitudinal ventilation, we have proposed a mathematic model of which the longitudinal ventilation can be described by the grey system with a grey cause and white result. By means of the grey theory, a grey prediction method to establish the discrete grey model DGM (1, 1) has been proposed to forecast the air pollutions in road tunnels. Combining with moving average smooth method, the proposed method is used to predict CO concentrations in China's Qinling No.1 tunnel separately for one minute and ten minutes. The application results show that the maximum relative error of the grey prediction method is less than 5% in one minute forecast and is less than 10% in ten minutes forecast, and the mean absolute percentage errors is only 0.89% for one minute prediction and 3.16% for ten minutes prediction.
{"title":"Grey Difference Model to Forecast Air Pollution in Road Tunnel","authors":"Jian-Tao Chen, Yunhua Li","doi":"10.30016/JGS.201009.0002","DOIUrl":"https://doi.org/10.30016/JGS.201009.0002","url":null,"abstract":"Longitudinal ventilation system of the long tunnel in the highway is a random, sluggish and nonlinear system. To accurately predict air pollution concentration in the road tunnel is very useful and necessary for us to improve the efficiency and the quality of ventilation control system. In this paper, based on having thoroughly analyzed the physical process of the longitudinal ventilation, we have proposed a mathematic model of which the longitudinal ventilation can be described by the grey system with a grey cause and white result. By means of the grey theory, a grey prediction method to establish the discrete grey model DGM (1, 1) has been proposed to forecast the air pollutions in road tunnels. Combining with moving average smooth method, the proposed method is used to predict CO concentrations in China's Qinling No.1 tunnel separately for one minute and ten minutes. The application results show that the maximum relative error of the grey prediction method is less than 5% in one minute forecast and is less than 10% in ten minutes forecast, and the mean absolute percentage errors is only 0.89% for one minute prediction and 3.16% for ten minutes prediction.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70059980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-06-01DOI: 10.30016/JGS.201006.0003
Rih-Chang Chao, Bor-Chen Kuo, Ya-Hsun Tsai
In this paper, the samples are randomly selected from a CSL (Chinese as second language) computerized test. Follow by performing utilization of Grey Relational Analysis (GRA) to calibrate and analysis the rank of each item difficulty. The major objective of this paper is to compare the rank difference between method of GRA under limited samples and Rasch model with sufficient data available in Item Response Theory. All data was collected from a CSL computerized test conducted overseas in Philippine during 19(superscript th) to 24(superscript th) of October 2009. There were 269 examinees participated in this test. Our study aimed to use GRA on decision making under uncertainty and with insufficient or limited data available for analysis and to prove its effectiveness. This analyzing procedure will contribute and re-productively applied into other areas, such as ”minimum sample requested for pre-testing” during the test item assembling in the futures.
{"title":"Item Ranking Comparison between GRA and IRT Rasch Model","authors":"Rih-Chang Chao, Bor-Chen Kuo, Ya-Hsun Tsai","doi":"10.30016/JGS.201006.0003","DOIUrl":"https://doi.org/10.30016/JGS.201006.0003","url":null,"abstract":"In this paper, the samples are randomly selected from a CSL (Chinese as second language) computerized test. Follow by performing utilization of Grey Relational Analysis (GRA) to calibrate and analysis the rank of each item difficulty. The major objective of this paper is to compare the rank difference between method of GRA under limited samples and Rasch model with sufficient data available in Item Response Theory. All data was collected from a CSL computerized test conducted overseas in Philippine during 19(superscript th) to 24(superscript th) of October 2009. There were 269 examinees participated in this test. Our study aimed to use GRA on decision making under uncertainty and with insufficient or limited data available for analysis and to prove its effectiveness. This analyzing procedure will contribute and re-productively applied into other areas, such as ”minimum sample requested for pre-testing” during the test item assembling in the futures.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70058587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-06-01DOI: 10.30016/JGS.201006.0005
Wei-Ling Liu
Early childhood education began in the 18 century, and was done mostly charity. Society has changed over the 30 years and now both parents need to work. Because of this, early childhood education is much more important. It is also more difficult to pick a good school because now parent have more choices, especially because there are schools all over Taiwan. In past research, we cannot find a clear method that helped parents choose quality school. Hence, in this paper, we use the grey relational grade, GM(h, N) and grey entropy as the mathematics models. The main purpose is to rank the influence factor for kindergarten and give suggestions to parents on the best way to pick a quality school. Based on the practical analysis, this study has made it possible to get access to the sequence and value of each variable. In addition, the result of this study is compatible with thoughts of individuals and parents may take the study result for the reference as making choices of kindergarten.
{"title":"The Weighting Analysis of Influence Factors in Kindergarten via Grey System Theory Method","authors":"Wei-Ling Liu","doi":"10.30016/JGS.201006.0005","DOIUrl":"https://doi.org/10.30016/JGS.201006.0005","url":null,"abstract":"Early childhood education began in the 18 century, and was done mostly charity. Society has changed over the 30 years and now both parents need to work. Because of this, early childhood education is much more important. It is also more difficult to pick a good school because now parent have more choices, especially because there are schools all over Taiwan. In past research, we cannot find a clear method that helped parents choose quality school. Hence, in this paper, we use the grey relational grade, GM(h, N) and grey entropy as the mathematics models. The main purpose is to rank the influence factor for kindergarten and give suggestions to parents on the best way to pick a quality school. Based on the practical analysis, this study has made it possible to get access to the sequence and value of each variable. In addition, the result of this study is compatible with thoughts of individuals and parents may take the study result for the reference as making choices of kindergarten.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70058593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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